SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection
arXiv:2605.23306v1 Announce Type: cross Abstract: Active traffic management (ATM) is frequently hindered by traditional macroscopic models and rigid empirical thresholds that fail to capture metastable phase precursors, resulting in delayed, reactive interventions. To address this, we propose SpinFlow, a physics-informed spin-field framework unifying Kerner's three-phase theory with statistical physics for continuous macroscopic traffic phase inference. Inspired by the Heisenberg model, SpinFlow parametrizes spatially varying phase weights via a latent spin vector and a competitive-equilibrium
The increasing complexity of urban environments and demand for efficient resource management are driving innovation in AI-powered predictive control systems.
This development can significantly improve urban planning, reduce congestion, optimize energy use in transportation, and pave the way for more sophisticated AI governance of physical infrastructure.
Traffic management shifts from reactive empirical thresholds to proactive, physics-informed, continuous phase inference, offering higher precision and adaptability.
- · Smart city developers
- · Urban planners
- · Logistics companies
- · AI infrastructure providers
- · Traditional traffic light manufacturers
- · Inefficient urban transport systems
- · Commuters in poorly managed cities
More efficient urban mobility and reduced traffic congestion in cities adopting this technology.
Potential for integration with other smart city systems, leading to holistic urban resource optimization.
The development of 'AI agents' capable of autonomous, real-time control over critical infrastructure, extending beyond traffic to other complex systems.
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Read at arXiv cs.LG